{"id":"W1570281671","doi":"10.1109/icc.2015.7248424","title":"Robustness of the routing protocol for low-power and lossy networks (RPL) in smart grid's neighbor-area networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer network; Computer science; Robustness (evolution); Routing protocol; Lossy compression; Smart grid; Distributed computing; Routing (electronic design automation); Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003794359,0.0001650984,0.0002165923,0.00003740613,0.00005468168,0.00003495671,0.0002209748,0.0001638767,0.00001406476],"category_scores_gemma":[0.00005733737,0.0001115472,0.00005622822,0.0002708283,0.00008437061,0.0001340015,0.00009194836,0.0002449693,2.963331e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000369688,"about_ca_system_score_gemma":0.0000276679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002770666,"about_ca_topic_score_gemma":0.0002482937,"domain_scores_codex":[0.9989941,0.00003153794,0.000308676,0.0001801005,0.000135512,0.0003500444],"domain_scores_gemma":[0.9994345,0.0001209503,0.00004880038,0.0002423794,0.00006398641,0.00008943042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003774034,0.00002604886,0.01318007,0.00006378755,0.000008846258,8.934186e-7,0.0001681315,0.9831252,0.000007472222,0.0003069858,0.002754808,0.0003200595],"study_design_scores_gemma":[0.0009842857,0.00003795245,0.005110204,0.0001865289,0.00000461289,0.000005241439,0.0001329818,0.9917932,0.0001629195,0.00003288045,0.001385581,0.000163623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2000491,0.0002945642,0.6308786,0.0003204819,0.004027335,0.1549484,0.00001056982,0.000423932,0.009047001],"genre_scores_gemma":[0.9804033,0.000003385782,0.0004026978,0.00004629043,0.0002886732,0.018768,0.000001804525,0.00002814698,0.0000576929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7803542,"threshold_uncertainty_score":0.4548763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655313531135993,"score_gpt":0.2367495695310974,"score_spread":0.2201964342197375,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}